title: Inferring model parameters in network-based disease simulation.
abstract: Many models of infectious disease ignore the underlying contact structure through which the disease spreads. However, in order to evaluate the efficacy of certain disease control interventions, it may be important to include this network structure. We present a network modeling framework of the spread of disease and a methodology for inferring important model parameters, such as those governing network structure and network dynamics, from readily available data sources. This is a general and flexible framework with wide applicability to modeling the spread of disease through sexual or close contact networks. To illustrate, we apply this modeling framework to evaluate HIV control programs in sub-Saharan Africa, including programs aimed at concurrent partnership reduction, reductions in risky sexual behavior, and scale up of HIV treatment.
authors.affiliation: Department of Electrical Engineering, Stanford University, Stanford, CA 94035-6019, USA. evaenns@stanford.edu
authors.forename: Eva A
authors.initials: EA
authors.lastname: Enns
authors.forename: Margaret L
authors.initials: ML
authors.lastname: Brandeau
mesh_terms: D017954:Africa South of the Sahara / Q000453:epidemiology; D000367:Age Factors; D003198:Computer Simulation; D015658:HIV Infections / Q000453:epidemiology / Q000517:prevention & control; D006298:Health Services Administration / Q000706:statistics & numerical data*; D006801:Humans; D008962:Models, Theoretical; D012309:Risk-Taking; D012737:Sex Factors; D012725:Sexual Behavior; D012959:Socioeconomic Factors; D013269:Stochastic Processes